Tutorial CUDA

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Example: Increment Array Elements Increment N-element vector a by scalar b Let’s assume N=16, blockDim=4 -> 4 blocks blockIdx.x=0 blockDim.x=4 threadIdx.x=0,1,2,3 idx=0,1,2,3 blockIdx.x=1 blockDim.x=4 threadIdx.x=0,1,2,3 idx=4,5,6,7 blockIdx.x=2 blockDim.x=4 threadIdx.x=0,1,2,3 idx=8,9,10,11 int idx = blockDim.x * blockId.x + threadIdx.x; will map from local index threadIdx to global index NB: blockDim should be >= 32 in real code, this is just an example blockIdx.x=3 blockDim.x=4 threadIdx.x=0,1,2,3 idx=12,13,14,15 Common Pattern! © NVIDIA Corporation 2008 26
Example: Host Code // allocate host memory unsigned int numBytes = N * sizeof(float) float* h_A = (float*) malloc(numBytes); // allocate device memory float* d_A = 0; cudaMalloc((void**)&d_A, numbytes); // copy data from host to device cudaMemcpy(d_A, h_A, numBytes, cudaMemcpyHostToDevice); // execute the kernel increment_gpu>(d_A, b); // copy data from device back to host cudaMemcpy(h_A, d_A, numBytes, cudaMemcpyDeviceToHost); // free device memory cudaFree(d_A); © NVIDIA Corporation 2008 27
Page 1 and 2: Tutorial CUDA Cyril Zeller NVIDIA D
Page 3 and 4: © NVIDIA Corporation 2008 GPU Comp
Page 5 and 6: Parallel Computing’s Dark Age But
Page 7 and 8: Enter the GPU GPU = Graphics Proces
Page 9 and 10: Enter CUDA CUDA is a scalable paral
Page 11 and 12: 110-240X © NVIDIA Corporation 2008
Page 13 and 14: GPUs Are Getting Faster, Faster ©
Page 15 and 16: © NVIDIA Corporation 2008 CUDA Pro
Page 17 and 18: Heterogeneous Programming CUDA = se
Page 19 and 20: Hierarchy of Concurrent Threads Thr
Page 21 and 22: Memory Hierarchy Sequential Kernels
Page 23 and 24: CUDA Language: C with Minimal Exten
Page 25: Example: Increment Array Elements C
Page 29 and 30: More on Memory Spaces Each thread c
Page 31 and 32: Compiling CUDA for NVIDIA GPUs Any
Page 33 and 34: Device Emulation Mode Pitfalls Emul
Page 35 and 36: Reduction Exercise At the end of ea
Page 37 and 38: Reduce 1: Blocking the Data Block I
Page 39 and 40: Reduce 1: Multi-Pass Reduction Bloc
Page 41 and 42: © NVIDIA Corporation 2008 CUDA Imp
Page 43 and 44: Hardware Implementation: A Set of S
Page 45 and 46: Hardware Implementation: Execution
Page 47 and 48: Host Synchronization All kernel lau
Page 49 and 50: Multiple CPU Threads and CUDA CUDA
Page 51 and 52: Performance Optimization Expose as
Page 53 and 54: Expose Parallelism: CPU/GPU Paralle
Page 55 and 56: Minimize CPU ↔ GPU Data Transfers
Page 57 and 58: Global Memory Reads/Writes Global m
Page 59 and 60: Coalesced Global Memory Accesses ©
Page 61 and 62: Non-Coalesced Global Memory Accesse
Page 63 and 64: Avoiding Non-Coalesced Accesses For
Page 65 and 66: Profiler Signals Events are tracked
Page 67 and 68: Back to Reduce Exercise: Profile wi
Page 69 and 70: Reduce 2 Thread IDs Block IDs Distr
Page 71 and 72: Maximize Use of Shared Memory Share
Page 73 and 74: Example: Square Matrix Multiplicati
Page 75 and 76: Example: Avoiding Non-Coalesced flo
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Example: Avoiding Non-Coalesced flo
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Example: Avoiding Non-Coalesced flo
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Execution Configuration: Constraint
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Execution Configuration: Heuristics
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Back to Reduce Exercise: Problem wi
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Parallel Reduction Complexity Takes
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Reduce 3: Go Ahead! Open up reduce\
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Arithmetic Instruction Throughput f
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Runtime Math Library There are two
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What You Need To Know FP64 instruct
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Mixed Precision Arithmetic Research
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Single Precision Floating Point ©
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Instruction Predication Comparison
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Shared Memory Is Banked Bandwidth o
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Bank Addressing Examples 2-way bank
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Back to Reduce Exercise: Problem wi
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Reduce 3: Bank Conflicts Showed for
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Reduce 4: Go Ahead! Open up reduce\
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Reduce 5: Unrolled Loop if (numThre
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Reduce 5: Final Unrolled Loop if (n
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Coming Up Soon CUDA 2.0 © NVIDIA C
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Extra Slides
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Applications - Condensed 3D image a
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Performance GPU Electromagnetic Fie
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EvolvedMachines 130X Speed up Brain
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nbody Astrophysics Astrophysics res
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A quick review device = GPU = set o
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Language Extensions: Function Type
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Language Extensions: Execution Conf
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Common Runtime Component Provides:
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Common Runtime Component: Mathemati
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Host Runtime Component Provides fun
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Host Runtime Component: Memory Mana
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Host Runtime Component: Interoperab
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Host Runtime Component: Error Handl
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Device Runtime Component: Mathemati
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Device Runtime Component: Texture F
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Compilation Any source file contain
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Role of Open64 Open64 compiler give
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CUDA Libraries CUBLAS CUFFT © NVID
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CUFFT Library Efficient implementat
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Tutorial CUDA

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